Efficient Spectrum Allocation Research Articles

Federated learning for efficient spectrum allocation in open RAN

Federated learning for efficient spectrum allocation in open RAN

Intelligent dynamic spectrum access using fuzzy logic in cognitive radio networks

Cognitive radio networks have emerged as a promising solution to the growing problem of spectrum scarcity and inefficient spectrum utilization. By enabling secondary users to opportunistically access underutilized frequency bands while avoiding harmful interference to primary users, cognitive radio networks offer the potential for more efficient and dynamic spectrum allocation. In this paper, we propose an intelligent approach to dynamic spectrum access using fuzzy logic in cognitive radio networks. The key challenge in cognitive radio networks lies in spectrum sensing, decision-making, and channel selection, where uncertainties and variations in the radio frequency spectrum are prevalent. To address these challenges, we employ fuzzy logic as a powerful tool to handle the inherent imprecision and ambiguity in the decision-making process. Fuzzy-based spectrum sensing algorithms allow for robust detection of spectrum opportunities by considering the “fuzziness” in the received signal strength and noise conditions.

Efficient spectrum allocation for secondary users in cognitive radio network using round robin priority algorithm along with reservation channels

Efficient spectrum allocation for secondary users in cognitive radio network using round robin priority algorithm along with reservation channels

An incentive-based dynamic energy efficient spectrum allocation for cognitive radio networks

Cognitive radio is a successful technique for utilizing the unused and under-used spectrum, and dynamic spectrum access is one of the major facilitators in making this happen. When a secondary user (an unlicensed user) interferes with the licensed user, the idea of using unused or under-utilized spectrum offers a challenge. Therefore, effective spectrum sensing is necessary to ensure the primary user’s protection and the successful transmission of data by the secondary user. An Optimal Incentive algorithm is suggested to meet this need. It effectively uses the available idle channel based on the joint optimization of sensing time and transmission time without interfering with the primary user. The proposed work also contributes to a significant increase in energy efficiency with minimal interference. Simulation results show an increase in efficiency when compared with the algorithms, namely, exhaustive search and sub-optimal algorithms.

Blocking Rate Analysis in Heterogeneous Multicell Cognitive-Radio 5G Networks

Cognitive radio (CR) technology is being recognized as one of the main enabling technologies for supporting massive high-speed connectivity in multicell fifth-generation (5G) networks by allowing dynamic spectrum access policies according to predetermined etiquette. Under this paradigm, the available spectrum is opportunistically allocated to the different cells. A key metric that is heavily employed in designing efficient CR-based multicell spectrum-allocation mechanisms is the blocking rate. An efficient spectrum-allocation mechanism aims to adaptively allocate channels to the different cells such that the overall blocking rate is minimized. In this article, we use the theory of continuous-time Markov chains to derive a closed-form expression for the blocking rate in multicell CR-based 5G networks. In our derivations, we build two detailed mathematical models describing the frequency-spectrum usage of both primary and secondary users. We combine the two models in a hierarchical fashion. As opposed to related models in the literature which suffer high complexity, the proposed hierarchical structure yields simplicity and tractability. Furthermore, while existing research works overlook spectrum heterogeneity, the proposed model addresses this important factor. Simulation results demonstrate high accuracy in evaluating the blocking rate across the different cells in the network.

Low Earth Orbit Satellite Communications for Internet-of-Things Applications

Internet of Things (IoT) is a new direction for future internet, where anything embedded with sensors, software, and transceiver can access the Internet. The applications of IoT technology include home automation, smart city, smart grid, smart industry, E-health, environmental monitoring, and geologic disaster forecasting, to name a few. As the demand for high data rate increases due to the huge number of IoT devices, the so-called Low Power Wide Area Networks (LPWAN) have emerged. These LPWAN are designed to cover wide terrestrial areas. Nevertheless, in open areas such deserts, forests and coastal waters of seas, rivers and oceans, there are cost and practical limitation in constructing LPWAN. Therefore, to enable LPWAN in such remote areas, Low Earth Orbit (LEO) satellite communications for IoT have recently attracted considerable interest from researchers. Among the open research issues are interference mitigation, Doppler spread estimation, LEO satellite constellation structure, efficient spectrum allocation, and access and routing protocols. In particular, as the satellite IoT system shares the same frequency band with the existing terrestrial IoT networks and other wireless systems, this results in multiple access interference issue. In addition, the high speed motion of LEO satellites induce a Doppler shift, resulting in inter-carrier interference problem.

Low Earth Orbit Satellite Communications for Internet-of-Things Applications

Internet of Things (IoT) is a new direction for future internet, where anything embedded with sensors, software, and transceiver can access the Internet. The applications of IoT technology include home automation, smart city, smart grid, smart industry, E-health, environmental monitoring, and geologic disaster forecasting, to name a few. As the demand for high data rate increases due to the huge number of IoT devices, the so-called Low Power Wide Area Networks (LPWAN) have emerged. These LPWAN are designed to cover wide terrestrial areas. Nevertheless, in open areas such deserts, forests and coastal waters of seas, rivers and oceans, there are cost and practical limitation in constructing LPWAN. Therefore, to enable LPWAN in such remote areas, Low Earth Orbit (LEO) satellite communications for IoT have recently attracted considerable interest from researchers. Among the open research issues are interference mitigation, Doppler spread estimation, LEO satellite constellation structure, efficient spectrum allocation, and access and routing protocols. In particular, as the satellite IoT system shares the same frequency band with the existing terrestrial IoT networks and other wireless systems, this results in multiple access interference issue. In addition, the high speed motion of LEO satellites induce a Doppler shift, resulting in inter-carrier interference problem.

Low Earth Orbit Satellite Communications for Internet-of-Things Applications

Internet of Things (IoT) is a new direction for future internet, where anything embedded with sensors, software, and transceiver can access the Internet. The applications of IoT technology include home automation, smart city, smart grid, smart industry, E-health, environmental monitoring, and geologic disaster forecasting, to name a few. As the demand for high data rate increases due to the huge number of IoT devices, the so-called Low Power Wide Area Networks (LPWAN) have emerged. These LPWAN are designed to cover wide terrestrial areas. Nevertheless, in open areas such deserts, forests and coastal waters of seas, rivers and oceans, there are cost and practical limitation in constructing LPWAN. Therefore, to enable LPWAN in such remote areas, Low Earth Orbit (LEO) satellite communications for IoT have recently attracted considerable interest from researchers. Among the open research issues are interference mitigation, Doppler spread estimation, LEO satellite constellation structure, efficient spectrum allocation, and access and routing protocols. In particular, as the satellite IoT system shares the same frequency band with the existing terrestrial IoT networks and other wireless systems, this results in multiple access interference issue. In addition, the high speed motion of LEO satellites induce a Doppler shift, resulting in inter-carrier interference problem.

Improved spectrum allocation scheme for TV white space networks using a hybrid of firefly, genetic, and ant colony optimization algorithms

This study proposes a novel hybrid Firefly Algorithm, Genetic Algorithm, and Ant Colony Optimization Algorithm (FAGAACO) for spectrum allocation in TV White Space (TVWS) networks. The Genetic Algorithm (GA) was used in the design to provide cross-over chromosomes to both the Firefly Algorithm (FA) and the Ant Colony Optimization Algorithm (ACO), thereby improving the exploration abilities of FA and ACO and preventing FA and ACO from becoming trapped in local optimum. The proposed algorithm was implemented using MATLAB R2018a. Simulation results show that in comparison with a hybrid of the Firefly Algorithm and Genetic Algorithm (FAGA), the proposed algorithm achieved 13.03% higher throughput, 1.3% improved objective function value and 5.03% higher runtime due to the good accuracy of the proposed algorithm. Based on these improvements, the proposed algorithm is therefore an efficient spectrum allocation technique in TVWS networks.

Learning to Optimize User Association and Spectrum Allocation With Partial Observation in mmWave-Enabled UAV Networks

To support large-scale unmanned aerial vehicle (UAV) networks with both payload communication (PC) packets and control and non-payload communication (CNPC) packets, millimeter wave (mmWave) communication is convinced to be a promising solution. However, efficient user association and spectrum allocation are still challenging in mmWave-enabled UAV networks considering that the network state is dynamic and the status information of each UAV is incomplete. In this paper, we investigate a joint UAV association and spectrum allocation problem under a hybrid mmWave sharing paradigm, where PC packets are transmitted over both licensed and pooled bands in a shared manner to achieve high throughput and CNPC packets are transmitted over licensed band in an exclusive manner to guarantee high reliability. To this end, we introduce a strategic form game that can characterize network stochastic states and individual partial observations to reformulate the problem, and then propose a counterfactual regret minimization scheme to achieve its correlated equilibrium (CE). Benefited from the updating mechanism on random <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">observation-action</i> pairs, the designed scheme can converge to the corresponding CE solutions for the two types of packets with partial observation. Finally, our simulation results demonstrate the superior performance of the proposed scheme over the baseline schemes.

Low complexity multichannel spectrum prediction algorithm based on optimized neural network for spectrum allocation in cognitive radio internet of things

AbstractCognitive radio has been nominated as a key technology for the internet of things (IoT), due to its intelligent functionalities ensuring continuous connectivity for IoT objects. Spectrum prediction, as one of the core CR functions, has emerged as a leading tool to alleviate the spectrum scarcity problem. Spectrum prediction minimizes sensing and decision‐making delays, and thereby it reduces collisions with primary users and guarantees safe access for secondary users (SUs). Thus, it became an inseparable part of many new spectrum allocation and mobility methods. In this work, the proposed cognitive radio IoT model consists of local sensors (LS) that perform sensing instead of SUs, and a cognitive base station that receives sensing results from different LSs to predict the next occupancy information and allocate frequencies for SUs. The predictor is followed by a channel extraction block for efficient spectrum allocation. Then, a low complexity spectrum prediction and preallocation system based on optimized neural network architecture is presented. Two nonlinear neural network models, time delay neural network and nonlinear autoregressive with exogenous input, that are trained on a real spectral occupancy dataset, are optimized using the Bayesian optimization algorithm then compared. The best predictor forecasts the next occupancy rate of multiple channels simultaneously based on three dimensions, area, time, and frequency. Performance evaluation was conducted through accuracy, mean squared error (MSE), and regression fit. The highest prediction accuracy was93.5%, the regression coefficient was0.98, and a reduced MSE of0.0013obtained. Results show that the considered scheme is efficient in forecasting the spectrum availability of different bands within the IoT spectrum resources.

Energy Aware Multipath Routing Protocol for Cognitive Radio Ad Hoc Networks

Cognitive radio networks (CRNs) emerged as a paradigm to solve the problem of limited spectrum availability and the spectrum underutilization in wireless networks by opportunistically exploiting portions of the spectrum temporarily vacated by licensed primary users (PUs). Routing in CRNs is a challenging problem due to the PU activities and mobility. On the other hand, energy aware routing is very important in energy-constraint CRNs. In addition, it is crucial that CR users efficiently exchange data with each other before the appearance of PUs. To design a robust routing scheme for mobile CR ad hoc networks (CRANs), the constraints on residual energy of each CR user, reliability, and the protection of PUs must additionally be taken into account. Moreover, multipath routing has great potential for improving the end-to-end performance of ad hoc networks. Considering all these evidences, in this paper, we propose an energy aware on-demand multipath routing (EOMR) protocol for mobile CRANs to ensure the robustness and to improve the throughput. The proposed routing scheme involves energy efficient multipath route selection and spectrum allocation jointly. The simulation results show that our approach improves the overall performance of the network.

An underwater cognitive acoustic network strategy for efficient spectrum utilization

In recent years, oceanic based research is gradually growing across the globe. Most researchers in this field aim at resolving the major challenges of Underwater Acoustic Sensor Networks like limited bandwidth, high propagation delay, 3D topology, media access control, routing, resource utilization, and power constraint neglecting spectrum utilization. However, most UAN designs consider the acoustic communication in underwater as a single network and only a very few consider the existence of other multiple acoustic systems in the ocean. Both the natural acoustic systems and artificial acoustic systems in the ocean use the acoustic means of communication leading to a heavily overcrowded spectrum. Despite the oversharing of channels the spectrum is still temporally and spatially underutilized. Unlike cognitive radio, spectrum allocation in cognitive acoustics is highly challenging due to the unique features of the underwater channel and the acoustic systems. The increasing intervention of anthropogenic activities in the oceans has resulted in an overshared, scarce, and inefficiently utilized spectrum. As an effort to calm down these adverse impacts, an underwater cognitive acoustic network-based spectrum decision strategy is proposed in this paper to aid in efficient spectrum utilization and allocation by multiple acoustic systems in the underwater environment. The proposed strategy is aimed at an environmentally friendly spectrum utilization model with much emphasis given to the primary users, the marine species. This paper considers all possible marine species and not just limit it to marine mammals. Finally, the scope of this paper is to provide an in-depth view of cognitive acoustics in UASN that can pay way for environmentally safe underwater research and marine habitat conservation in the future.

Inefficiency in spectrum auctions – empirical evidence

Purpose: The spectrum is scarce recourses, so it is very important to allocate it in a way to maximize the economic and social returns to the countries` economies. Economists agree that the best way to allocate rare resources are auctions. Despite their good properties, in some environments, they can create inefficiency. We describe some potential sources of inefficiency in spectrum auctions and some negative effects of inefficient spectrum allocation. We also discuss how the government through appropriate spectrum policy and auction designing can promote efficient spectrum allocation. Design/methodology/approach: The paper is based on the literature on the spectrum auction and public documents published by national spectrum authorities. Theoretical discussions are supported by case studies of real world spectrum auctions. Much attention has been paid to the case of the Polish 4G auction. Findings: Extremely high reserve prices or rigorous approach to promoting entry could lead to price distortions, misallocation or even unsold spectrum which harm both competition and consumers. The case of the Polish 4G auction confirms that gaps in the detailed auction rules may lead to significant delays in frequency allocation and increase the risk of an ineffective auction outcome. Practical implications: In the light of our findings, it is important that the rules of the auction in the 3.6 GHz band currently planned in Poland should allow the regulator to control the pace of the auction and take into account the risk of inefficient outcome associated with passive bids. Originality/value: The discussions made in this paper could help policy makers in Poland and other countries in spectrum auction designing

Reciprocating Reflective Double Gratings Based LCOS Spectral Filter With Sharp Response

Optical filter based on liquid crystal on silicon (LCOS) is widely used in wavelength selective switch (WSS) to allocate the optical spectrum flexibly. Constrained by the achievable spectral resolution of single grating used in the WSS, the roll-off bandwidth of optical filter is challenging to improve for the purpose of precise and efficient spectrum allocation. In this article, we demonstrate a novel double gratings configuration to realize higher spectral resolution by repeatedly enlarging and accumulating the dispersion, under the condition of the maintenance of the final light spot size. After examining the filtering performance with respect to the diffraction number, we are able to achieve a spectral resolution of 1.63 GHz, indicating of almost tenfold enhancement in comparison with traditional single grating configuration. In particular, an LCOS-based optical filter with a spectral roll-off bandwidth of 1.26 GHz and addressable resolution of 187 MHz is demonstrated. Meanwhile, the insertion loss and the range of operation wavelength are investigated to determine the optimal number of diffractions for various applications. The proposed LCOS-based filter enables spectral manipulation of GHz roll-off bandwidth, fulfilling the performance requirements of future flexible optical networking.

Strategy-Proof Spectrum Allocation Among Multiple Operators in Wireless Networks

The exponentially increasing demand for data necessitates efficient spectrum allocation among operators in wireless networks. In this paper, we address the spectrum allocation problem among non-cooperative operators via auctions. The classical Vickrey-Clarke-Groves (VCG) approach provides the framework for a strategy-proof and social welfare maximizing auction. However, the VCG mechanism has high computational complexity, which makes it infeasible for practical implementation. In this work, we propose sealed bid auction mechanisms for spectrum allocation, which are computationally tractable. These can be used for spectrum allocation by performing auctions at shorter intervals to cater to the dynamic load variation in the network. We establish that the proposed algorithm is strategy-proof for the uniform demand scenario. Furthermore, for non-uniform demand, we propose an algorithm that satisfies weak strategy-proofness. Here, we also consider non-linear increase in the marginal valuations with demand. Simulation results are also presented to exhibit the performance comparison of the proposed algorithms with VCG and other existing mechanisms.

Invalid-Resource-Aware Spectrum Assignment for Advanced-Reservation Traffic in Elastic Optical Network

Elastic optical networks (EONs) can make service accommodation more flexible and precise by employing efficient routing and spectrum allocation (RSA) algorithms. In order to improve the efficiency of RSA algorithms, the advanced-reservation technique was introduced into designing RSA algorithms. However, few of these advanced-reservation-based RSA algorithms were focused on the unavailable spectrum resources in EONs. In this paper, we propose an Advanced-Reservation-based Invalid-Spectrum-Aware (AR-ISA) resource allocation algorithm to improve the networking performance and the resource alignment of EONs. By employing a new index, Invalid Spectrum Rate (ISR), to record the proportion of unavailable spectrum resources in EONs, the proposed AR-ISA algorithm set a network load threshold to trigger the postponement of an arriving service. Compared with the traditional slack-based AR mechanism, the proposed algorithm has more concerns about the current spectrum usage of the path designated by the service than the conflicts between AR services and other existing services. To further increase the networking performance, the proposed algorithm adopts defragmentation to increase the number of available spectrum resources when postponing a service. Theoretical analysis and simulation results show that the proposed AR-ISA algorithm has obvious effectiveness in reducing the service blocking rate and increasing the spectrum alignment rate.

Hybrid Optimization Algorithms for Resource Allocation in Heterogeneous Cognitive Radio Networks

In recent days, accessing the spectrum of Cognitive Radio spread has gained significance in the applications of sensor based wireless devices. In this promising environment the bottle neck existed in accessing the spectrum which makes complications in the setup on wireless networks. In each cognitive space the spread spectrum optimization to the entire user is much more needed for enjoying the fruits of cognitive Radio. To achieve this, an effective mechanism is needed for the effective utilization of spectrum in cognitive radio system. Even though there are many substantial innovations in the area of cognitive radio none of the prevailing articles have focused on providing solutions to the problems which are related to efficient allocation of spectrum for each subordinate user with less power utilization and interference. In this article, the framework has been analyzed in both presence and absence of prime users with maximizing the capacity and data rate of the network. Further, the proposed model is solved by integrating a hybrid optimization algorithm with effective decision-making mechanism. Moreover, the simulation results validate that best solution is achieved considering capacity, spectrum sharing, data rate and interference for each subordinate users in entire network.

A novel hybrid learning based Ada Boost (HLBAB) classifier for channel state estimation in cognitive networks

Spectrum sensing, through efficient channel estimation methods utilizing cognitive radio networks, has become an increasingly researched area in recent times. This has become more pronounced in recent times, especially with increasing scarcity in availability of radio frequency spectrum. Wireless state of the art communication standards demand high bandwidth to provide seamless connectivity and high degree of mobility, which requires more of radio frequency band for functioning. Hence, intelligent methods of spectrum allocation have been an increasing challenge in recent times. This paper proposes a hybrid learning based-Ada Boost classifier model for efficient spectrum allocation through channel state estimation through a learning and double classification approach. The proposed algorithm has been experimented in a high bandwidth characterized 5G communication simulation settings and observed for its performance measures namely collision rate analysis, throughput, probability of detection, false alarm detection and bit error rate. The proposed technique has been compared against benchmark techniques such as conventional fast Fourier transform based energy detector, fuzzy cognitive engine and adaptive neuro fuzzy inference model without Ada Boost and found to exhibit superior performance in all performance measures. The proposed technique exhibits a spectral efficiency of nearly 90% and considered to be a suitable spectrum sensing scheme for high bandwidth and narrowband utilities.

Federal Communications Commission’s experimental radio service as a vehicle for dynamic spectrum access: An analysis of 10 years of experimental licenses data

AbstractDynamic spectrum access (DSA) systems, commonly known as spectrum sharing, are considered one of the most promising paths for more efficient spectrum allocation. When talking about DSA, the most discussed topics revolve around particular technologies such as cognitive radios or particular solutions such as the advanced wireless services-3 initiative. However, in this work, we explore a less discussed approach for spectrum sharing: the Federal Communications Commission (FCC)’s experimental radio service (ERS). The ERS grants licenses for experimentation, market trials, and product development in Federal and/or non-Federal bands. Frequencies in these licenses are assigned on a shared basis and not for the exclusive use of any one licensee. Using FCC’s scraped information in the period between 2007 and 2016, we were able to gain a deeper understanding of the ERS. We find that the processing time (i.e., time to get a license) has been reduced from 100 days to an average of 23 days in 2016. Moreover, the assignation process of experimental licenses is characterized great flexibility in terms of the authorized technical and nontechnical characteristics. We also explored what is behind these 10 years of information.